Efficient Replication of Over 180 Genetic Associations with Self-Reported Medical Data

While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web-based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in-depth questions to refine self-reported diagnoses. Our data suggests that online collection of self-reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations

Efficient Replication of Over 180 Genetic Associations with Self‐Reported Medical Data

While the cost and speed of generating genomic data have come down dramatically in recent years, the slow pace of collecting medical data for large cohorts continues to hamper genetic research. Here we evaluate a novel online framework for amassing large amounts of medical information in a recontactable cohort by assessing our ability to replicate genetic associations using these data. Using web‐based questionnaires, we gathered self-reported data on 50 medical phenotypes from a generally unselected cohort of over 20,000 genotyped individuals. Of a list of genetic associations curated by NHGRI, we successfully replicated about 75% of the associations that we expected to (based on the number of cases in our cohort and reported odds ratios, and excluding a set of associations with contradictory published evidence). Altogether we replicated over 180 previously reported associations, including many for type 2 diabetes, prostate cancer, cholesterol levels, and multiple sclerosis. We
found significant variation across categories of conditions in the percentage of expected associations that we were able to replicate, which may reflect systematic inflation of the effects in some initial reports, or differences across diseases in the likelihood of misdiagnosis or misreport. We also demonstrated that we could improve replication success by taking advantage of our recontactable cohort, offering more in‐depth questions to refine self‐reported diagnoses. Our data suggests that online collection of self‐reported data in a recontactable cohort may be a viable method for both broad and deep phenotyping in large populations

Dual targeting of the chemokine receptors CXCR4 and ACKR3 with novel engineered chemokines

The chemokine CXCL12 and its G protein-coupled receptors CXCR4 and ACKR3 are implicated in cancer and inflammatory and autoimmune disorders and are targets of numerous antagonist discovery efforts. Here, we describe a series of novel, high affinity CXCL12-based modulators of CXCR4 and ACKR3 generated by selection of N-terminal CXCL12 phage libraries on live cells expressing the receptors. Twelve of 13 characterized CXCL12 variants are full CXCR4 antagonists, and four have Kd values <5 nm. The new variants also showed high affinity for ACKR3. The variant with the highest affinity for CXCR4, LGGG-CXCL12, showed efficacy in a murine model for multiple sclerosis, demonstrating translational potential. Molecular modeling was used to elucidate the structural basis of binding and antagonism of selected variants and to guide future designs. Together, this work represents an important step toward the development of therapeutics targeting CXCR4 and ACKR3.Melinda S. Hanes, Catherina L. Salanga, Arnab B. Chowdry, Iain Comerford, Shaun R. McColl, Irina Kufareva, and Tracy M. Hande